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Background Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning model can predict tumor response to NAC. Methods Patients with pancreatic cancer receiving neoadjuvant therapy prior to pancreatoduodenectomy were identified between November 2009 and January 2018. The College of American Pathologists Tumor Regression Grades 0-2 were defined as pathologic response (PR) and grade 3 as no response (NR). Axial images from preoperative computed tomography scans were used to create a 5-layer convolutional neural network and LeNet deep learning model to predict PRs. The hybrid model incorporated decrease in carbohydrate antigen 19-9 (CA19-9) of 10%. Accuracy was determined by area under the curve. Results A total of 81 patients were included in the study. Patients were divided between PR (333 images) and NR (443 images). The pure model had an area under the curve (AUC) of .738 (P < .001), whereas the hybrid model had an AUC of .785 (P < .001). CA19-9 decrease alone was a poor predictor of response with an AUC of .564 (P = .096). Conclusions A deep learning model can predict pathologic tumor response to neoadjuvant therapy for patients with pancreatic adenocarcinoma and the model is improved with the incorporation of decreases in serum CA19-9. Further model development is needed before clinical application.

The aim of this study was to investigate outcomes associated with neoadjuvant chemotherapy in patients undergoing pancreatoduodenectomy for early stage pancreatic adenocarcinoma in the era of modern chemotherapy. The National Cancer Database (2010–2016) was queried for patients with clinical stage 0–2 pancreatic adenocarcinoma who underwent pancreatoduodenectomy. Patients who underwent up-front pancreatoduodenectomy were propensity matched to patients who received neoadjuvant chemotherapy. Postoperative outcomes, pathologic outcomes, and overall survival were compared. A total of 2036 patients were in each group. Neoadjuvant chemotherapy was associated with shorter length of stay, lower 30-day readmission rate, and lower 30 and 90-day mortality rates (all p < 0.05). Neoadjuvant chemotherapy was associated with lower rates of positives nodes and positive resection margins (all p < 0.0001). Neoadjuvant chemotherapy was associated with longer survival (26.8 vs. 22.1months, p < 0.0001). Patients who received neoadjuvant chemotherapy followed by surgery and adjuvant therapy had the longest OS, followed by neoadjuvant + surgery, surgery + adjuvant therapy, and surgery alone (29.8 vs. 25.6 vs. 23.9 vs. 13.1 months; p < 0.0001). Neoadjuvant chemotherapy is associated with improved postoperative outcomes, oncologic outcomes, and overall survival in patients with early stage pancreatic adenocarcinoma. Neoadjuvant chemotherapy should be considered in all patients with early stage pancreatic adenocarcinoma. •Rate of use of neoadjuvant therapy is increasing in early stage pancreatic adenocarcinoma.•Neoadjuvant chemotherapy is associated with improved postoperative and pathologic outcomes.•Neoadjuvant chemotherapy is associated with longer overall survival (27 vs 22 months).•One-third of patients who undergo up-front resection do not receive adjuvant chemotherapy.•Those who receive both pre- and postoperative chemotherapy have the longest survival (30 months).

Background Roux-en-Y gastric bypass (RYGB) effectively treats obesity and gastroesophageal reflux disease (GERD). As more surgeons recommend RYGB to treat GERD in patients with obesity, there are concerns about this approach in patients with previous non-bariatric foregut surgery. This study aims to evaluate the effect of previous non-bariatric foregut surgery on subsequent RYGB. Methods Retrospective review of 2089 patients undergoing RYGB between January 1985 and June 2015 was conducted to identify all patients with previous non-bariatric foregut surgery. Perioperative and postoperative data was collected by retrospective chart review. Results A total of 11 patients with prior non-bariatric foregut surgery underwent RYGB with median time between operations of 95.6 months. Of note, 7/11 (63.6%) had previous Nissen fundoplication. Conversion to open operation was required in 3/7 (42.9%) with previous Nissen compared to 1/4 (25%) in those without previous Nissen. The average length of stay (LOS) was 3.9 ± 0.9 days, significantly longer than our institutional average for RYGB of 3.2 ± 3.2 days ( p  = 0.02). Mean percentage of excess body mass index loss (%EBMIL) was 64.7 ± 23.5 at 4-year median follow-up, comparable to our institution’s previously reported data. No mortalities were attributed to RYGB and the overall complication rate was 18.2%, compared to our institutional complication rate for RYGB of 8.5% ( p  = 0.253). Conclusion Despite increased technical difficulty and increase perioperative morbidity, patients undergoing RYGB with previous non-bariatric foregut surgery had long-term symptom resolution and robust weight loss. This indicates that in the right hands, RYGB after non-bariatric foregut surgery may be performed safely and effectively.

Background Sarcopenia, loss of muscle mass and strength, has been associated with more frequent complications after esophagectomy. This study compared hand-grip strength, muscle mass, and intramuscular adipose tissue as predictors of postoperative outcomes and mortality after esophagectomy. Methods Minimally invasive esophagectomy was performed on 175 patients with esophageal cancer. Skeletal muscle index and skeletal muscle density were derived from preoperative CTs. Hand-grip strength was measured using dynamometer. Univariate and multivariable analyses were performed. Results Preoperative hand-grip strength was normal in 91 (52%), intermediate in 43 (25%), and weak in 41 (23%) patients. Hand-grip strength was significantly correlated with both skeletal muscle index and skeletal muscle density. Postoperative pneumonia occurred in 8/41 (20%) patients with weak strength compared to 4/91 (4%) with normal strength ( p  = 0.006; Cochran-Armitage Test). Prolonged postoperative ventilation occurred in 11/41 (27%) patients with weak strength compared to 11/91 (12%) with normal strength ( p  = 0.036). Median length of stay was 9 days in patients with weak strength compared to 7 days for those with normal strength ( p  = 0.005; Kruskal–Wallis Test). Discharge to non-home location occurred in 15/41 (37%) with weak strength compared to 8/91 (9%) with normal strength ( p  < 0.001). Postoperative mortality at 90 days was 4/41 (10%) with weak strength compared with no mortalities (0/91) in the normal strength group ( p  = 0.004). Mortality at 1 year was 18/39 (46%) in patients with weak strength compared to 6/81 (7%) with normal strength, among 158 patients with 1-year follow-up ( p  < 0.001). Conclusions Preoperative hand-grip strength was found to be a powerful predictor of postoperative pneumonia, length of stay, discharge to non-home location, and mortality after esophagectomy.

Background Society consensus guidelines are commonly used to guide management of pancreatic cystic neoplasms (PCNs). However, downsides of these guidelines include unnecessary surgery and missed malignancy. The aim of this study was to use computed tomography (CT)-guided deep learning techniques to predict malignancy of PCNs. Materials and Methods Patients with PCNs who underwent resection were retrospectively reviewed. Axial images of the mucinous cystic neoplasms were collected and based on final pathology were assigned a binary outcome of advanced neoplasia or benign. Advanced neoplasia was defined as adenocarcinoma or intraductal papillary mucinous neoplasm with high-grade dysplasia. A convolutional neural network (CNN) deep learning model was trained on 66% of images, and this trained model was used to test 33% of images. Predictions from the deep learning model were compared to Fukuoka guidelines. Results Twenty-seven patients met the inclusion criteria, with 18 used for training and 9 for model testing. The trained deep learning model correctly predicted 3 of 3 malignant lesions and 5 of 6 benign lesions. Fukuoka guidelines correctly classified 2 of 3 malignant lesions as high risk and 4 of 6 benign lesions as worrisome. Following deep learning model predictions would have avoided 1 missed malignancy and 1 unnecessary operation. Discussion In this pilot study, a deep learning model correctly classified 8 of 9 PCNs and performed better than consensus guidelines. Deep learning can be used to predict malignancy of PCNs; however, further model improvements are necessary before clinical use.

Introduction Studies have shown that for patients with hilar cholangiocarcinoma (HC), survival is associated with negative resection margins (R0). This requires increasingly proximal resection, putting patients at higher risk for complications, which may delay chemotherapy. For patients with microscopically positive resection margins (R1), the use of modern adjuvant therapies may offset the effect of R1 resection. Methods Patients at our institution with HC undergoing curative-intent resection between January 2008 and July 2019 were identified by retrospective record review. Demographic data, operative details, tumor characteristics, postoperative outcomes, recurrence, survival, and follow-up were recorded. Patients with R0 margin were compared to those with R1 margin. Patients with R2 resection were excluded. Results Seventy-five patients underwent attempted resection with 34 (45.3%) cases aborted due to metastatic disease or locally advanced disease. Forty-one (54.7%) patients underwent curative-intent resection with R1 rate of 43.9%. Both groups had similar rates of adjuvant therapy (56.5% vs. 61.1%, P = .7672). Complication rates and 30 mortality were similar between groups (all P > .05). Both groups had similar median recurrence-free survival (R0 29.2 months vs. R1 27.8 months, P = .540) and median overall survival (R0 31.2 months vs. R1 38.8 months, P = .736) with similar median follow-up time (R0 29.9 months vs. R1 28.5 months, P = .8864). Conclusions At our institution, patients undergoing hepatic resection for HC with R1 margins have similar recurrence-free and overall survival to those with R0 margins. Complications and short-term mortality were similar. This may indicate that with use of modern adjuvant therapies obtaining an R0 resection is not an absolute mandate.

Background Gastrectomy is the cornerstone of treatment for gastric cancer. Recent studies demonstrated significant surgical outcome advantages for patients undergoing minimally invasive versus open gastrectomy. Lymph node harvest is an indicator of adequate surgical resection, and greater harvest is associated with improved staging and patient outcomes. This study evaluated lymph node harvest based on surgical approach. Methods Gastric adenocarcinoma patients were identified from NCDB who underwent gastrectomy between 2010 and 2016. Patients were classified by surgical approach into three cohorts: robotic, laparoscopic, or open gastrectomy. Clinical and demographic data were collected. Lymph node harvest was compared with univariate analysis and multivariable generalized linear mixed model. Univariate analysis with propensity matching was also performed to control for differences in patient population across cohorts. Results We identified 10,690 patients that underwent gastrectomy for gastric adenocarcinoma, with 68% males and median age of 66 (IQR 5774) years. 7161 (67%) underwent open, 2841 (26.6%) laparoscopic, and 688 (6.4%) robotic gastrectomy. Multivariable analysis revealed robotic was associated with a significantly higher median node harvest (18, IQR 1326) compared to laparoscopic (17, IQR 1125) and open gastrectomy (16, IQR 1023). Laparoscopic was also associated with significantly higher node harvest then open gastrectomy. Propensity-matched analysis (6950 patients) showed robotic gastrectomy was still associated with significantly higher node harvest (18, IQR 1226) compared to laparoscopic (17, IQR 1125) and open (17, IQR 1124); however, laparoscopic and open were not significantly different. Conclusion Robotic approach is associated with increased node harvest compared to laparoscopic and open approach in gastrectomy patients.

There has been increasing utilization of minimally invasive surgical approaches. This study evaluates the effect of surgical approach on total lymph node harvest in gastrectomy. Patients undergoing gastrectomy for gastric adenocarcinoma between 2007 and 2018 were reviewed retrospectively. Data collected included age, gender, race, BMI, neoadjuvant therapy, tumor stage, surgical approach, and total number of lymph nodes harvested. The total number of harvested lymph nodes for open, laparoscopic, and robotic gastrectomy was compared using the Kruskal-Wallis test for univariate analysis and a Poisson regression model for multivariable analysis. One hundred four patients were identified. Median node harvest for open, laparoscopic, and robotic approaches were 16, 17, and 36, respectively. Multivariable analysis controlling for gender, BMI, pathological T stage, and year of operation demonstrates that surgical approach is statistically significantly associated with lymph node harvest (F = 83.4, P < 0.0001). In multivariable analysis, robotic approach was associated with greater lymph node harvest than both open (P < 0.0001) and laparoscopic (P < 0.0001) approaches, whereas laparoscopic approach was associated with greater lymph node harvest than open (P < 0.0001) approach. These data demonstrate that for patients undergoing gastrectomy for gastric adenocarcinoma at our institution, robotic approach is associated with greater lymph node harvest than both laparoscopic and open approaches.

Background: Underutilization of operative management of early stage pancreatic cancer is associated with sociodemographic variables, including age, race, facility type, insurance, and education. It is currently unclear how these variables are associated with survival in patients who undergo surgery. Methods: Patients with clinical stage I pancreatic adenocarcinoma were identified within the National Cancer Database (2010–2016). Utilization of surgery and nonoperative management was determined. Nonclinical factors associated with nonoperative management were identified by multivariable analysis. The association between nonclinical factors and survival was assessed in patients who received operative management. Results: A total of 17,833 patients with clinical stage I pancreatic cancer were identified, and 41.2% underwent operative intervention. Approximately 46% of nonoperatively managed patients lacked a contraindication. Operatively managed patients had longer overall survival (OS) than those who were nonoperatively managed or untreated (25.1 months vs. 11.1 months vs. 5.1 months, p < 0.0001). Factors associated with nonoperative management included age, black/Hispanic race, nonacademic facilities, nonprivate health insurance, lower education level, and lower income. In operatively managed patients, nonclinical factors associated with lower OS included Medicaid (hazard ratio [HR] 1.27) and treatment at nonacademic facilities (HR 1.20–1.22). Patients on Medicaid received less adjuvant therapy and had higher 30- and 90-day mortality rates. Patients treated at nonacademic facilities received less neoadjuvant therapy, had worse pathologic outcomes, and had higher 30- and 90-day mortality rates. Conclusions: Surgical management is underutilized in clinical stage I pancreatic cancer. Primary insurance payor and facility type appear to be associated with OS in patients who undergo operative management.

A selection of cotton samples is tested with the Suter-Webb Array, AFIS (advanced fiber information system), and HVI (high volume instrument) methods. Short fiber contents as measured by these different methods show significant differences and high variations. The calibration level is one of the major factors causing these differences. Provided all other conditions are the same, a shift of 0.01 inch in fiber length calibration can cause an approximately 0.37% absolute value change in measured short fiber content based on the average of the test data. Based on the results from AFIS tests and computer simulation, sample nonuniformity, which is a characteristic of cotton fibers, contributes a major portion of the variation of the measured short fiber content.